Anirban Sengupta, Ph.D.

Research Instructor
Radiology and Radiological Science

My research interest lies at the intersection of MRI and fMRI data analysis and machine learning techniques. I am currently interested in using resting state fMRI for probing various clinically relevant neuroscience questions in primate models. My PhD research work involved devising methodology for tumor segmentation and grading in glioma patients using a combination of DCE-MRI and conventional MRI parameters under a machine learning framework.

For the current research projects, I analyze the resting state functional connectivity in monkey brain and spinal cord injury models to investigate changes in their functional circuits. I use data driven techniques such as ICA for detecting characteristic features from the fMRI signal of brain and spinal cord.

Research Information

Sengupta A, Wang F, Mishra A, Reed JL, Chen LM, Gore JC. Detection and characterization of resting state functional networks in squirrel monkey brain. Cereb Cortex Commun. 2023

Sengupta A, Mishra A, Wang F, Li M, Yang PF, Chen LM, Gore JC. Functional networks in non-human primate spinal cord and the effects of injury. Neuroimage. 2021. 

Gao Y, Sengupta A, Li M, Zu Z, Rogers BP, Anderson AW, Ding Z, Gore JC; Alzheimer's Disease Neuroimaging Initiative. Functional connectivity of white matter as a biomarker of cognitive decline in Alzheimer's disease. PLoS One. 2020

Sengupta A, Agarwal S, Gupta PK, Ahlawat S, Patir R, Gupta RK, Singh A. On differentiation between vasogenic edema and non-enhancing tumor in high-grade glioma patients using a support vector machine classifier based upon pre and post-surgery MRI images. Eur J Radiol. 2018

Sengupta A, Ramaniharan AK, Gupta RK, Agarwal S, Singh A. Glioma grading using a machine-learning framework based on optimized features obtained from T1 perfusion MRI and volumes of tumor components. J Magn Reson Imaging. 2019